Technical Field
The present disclosure relates to an apparatus that processes data including a plurality of spectra and a method for processing the data.
Description of the Related Art
Biological tissues include various substances. In order to detect differences in the composition and the chemical state of the substances, various spectra of a biological sample are measured, and the measured spectra are analyzed. By setting a plurality of measurement points in the biological sample and analyzing spectra measured at the measurement points, spatial distribution information such as the shapes and the composition of the biological tissues can be obtained without staining the biological sample.
Currently, multivariate analyses typified by a principal component analysis (PCA) and an independent component analysis (ICA) are adopted as methods for analyzing spectra.
Since a biological sample includes a plurality of components and tissues, spectra derived from these components are superimposed upon one another, and accordingly spectra obtained as a result of measurement of the biological sample are complex. By using the multivariate analyses, the spectra derived from the components of the sample can be separated from one another in these complex spectra, thereby making it possible to analyze the components and the composition.
In Y. Ozeki et al. “High-speed molecular spectral imaging of tissue with stimulated Raman scattering”, Nature Photonics 6, pp. 845-851 (2012), a method is described in which Raman spectra are measured at a plurality of measurement points in a biological sample and a PCA and an ICA are performed on data including the obtained plurality of spectra to obtain the spatial distribution of independent component scores. By using this method, spatial distribution information regarding components of the biological sample can be obtained. By using a different color for a spatial distribution image of each component and superimposing the spatial distribution images, the distribution of the components in the biological sample can be displayed in false colors.
In general, when a plurality of spectra of components of a sample are separated from one another in data including the plurality of spectra using an ICA, first, a separation matrix (base vectors) is obtained. Thereafter, the obtained separation matrix is applied to the spectra included in the data to obtain independent component scores.
The separation matrix is obtained by performing convergence calculation on a source matrix, which is obtained by applying the separation matrix to the data, in such a way as to maximize the statistical independence of each source vector in the source matrix. Therefore, as the amount of data subjected to the ICA, that is, the number of dimensions of spectra and the number of measurement points, increases, the amount of calculation and calculation time exponentially increase.